Optimization of Personalized Recommendation Strategy for E-commerce Platform Based on Artificial Intelligence
Abstract
In order to solve the problems of "resource overload" and "information confusion" in the current e-commerce platform, taking the collaborative filtering method as the basic algorithm, this paper proposes a personalized recommendation strategy for e-commerce platform based on artificial intelligence, optimizes the strategy, and obtains satisfactory results. Based on this proposed personalized recommendation model, users are clustered by using ontology context information, and the influence of user preference and user trust relationship on similarity calculation is considered. This method can alleviate the problems of data sparsity and cold start to a certain extent, effectively improve the recommendation quality, increase the diversity of recommendation results, and meet the needs of users and enterprises. Through the change of parameter α under different data sets, when α ∈ (1.84,1.88), the accuracy and recall rate of recommendation results remain at a high level. The personalized recommendation method can be applied to various situations such as social network friend recommendation and e-commerce platform commodity recommendation, and has a wide range of applications. Especially for enterprises that master the user's rich dimensional situation information, with the help of detailed analysis of the user's complex situation, this method has a prominent recommendation effect.
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DOI: https://doi.org/10.31449/inf.v47i2.3981
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